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1. Individual differences in relational reasoning

   https://doi.org/https://doi.org/10.3758/s13421-019-00964-y  

   Gray, Maureen E.; Holyoak, Keith J. (January 2020, Memory cognition)

   Various forms of relational processing have been linked to cognitive capacity measures, such as working memory and fluid intelligence. However, previous work has not established the extent to which different forms of relational processing reflect common factors, nor whether individual differences in cognitive style also contribute to variations in relational reasoning. The current study took an individual-differences approach to investigate the prerequisites for relational processing. In two studies, college students completed a battery of standardized tests of individual differences related to fluid intelligence and cognitive style, as well as a series of experimental tasks that require relational reasoning. Moderate correlations were obtained between relational processing and measures of cognitive capacity. Questionnaire measures of cognitive style generally did not improve predictions of relational processing beyond the influence of measures of cognitive capacity. 

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2. Relations Between Executive Functions and Causal Reasoning in Young Children

   https://doi.org/10.3102/1315192  

   Bauer, Jessie-Raye; Booth, Amy E. (April 2018, AERA)

   Despite the early development of causal reasoning (CR), and its potential for shaping scientific literacy, we have little understanding of its structural origins. Specifically, is CR a unique capability that develops relatively independently or is it largely dependent on broader, more fundamental, cognitive abilities? Executive Functioning (EF) is an especially promising contributor to CR based on its already established role in related skills like planning and problem solving (e.g., Diamond, 2013). To begin exploring this potential relationship, we assessed 123 three (Mage = 3.42 years) and 64 five year olds’ (Mage = 5.36 years) performance on two CR tasks (counterfactual reasoning and causal inference), each of which we expected might be influenced in different ways by distinct EF skills. The counterfactual reasoning task (Guajardo & Turley-Ames, 2004) required children to generate alternative courses of action that would lead to different outcomes in fictional vignettes. The causal inference task (Das Gupta & Bryant, 1989) required children to compare pictures taken before and after a transformation (e.g., broken flowerpot and intact flowerpot) and to select a tool (e.g., glue) that could have caused it. We measured EF with three tasks: flanker (inhibition), count and label (working memory), and dimensional change card sort (cognitive flexibility). Finally, we measured children’s vocabulary and processing speed. To explore the relationship between EF and CR, we conducted a series of four linear regressions predicting causal inference and counterfactual reasoning ability in 3 and 5 year olds. Of all our measures, only vocabulary and inhibitory control emerged as significant predictors of causal inference ability for both 3 (βvocab = .04, p = .002, and βinhib = .04, p = .04) and 5 year olds (βvocab = .03, p = .01, and βinhib = .02, p = .04). Similarly, inhibitory control emerged as the only significant predictor of counterfactual reasoning in 3 year olds, βinhib = .03, p = .03. In contrast, for 5 year olds, working memory was the only significantly predictor of counterfactual reasoning, βWM = .71, p = .02. These results suggest that causal inference skills are stably supported by inhibitory control throughout early childhood. The story for counterfactual reasoning, however, appears to be somewhat more complex. Consistent with previous work (Beck, Riggs & Gorniak, 2009), inhibitory control supported counterfactual reasoning ability in our 3-year-old sample. However, inhibitory control did not significantly predict counterfactual reasoning in 5 year olds, it was supported by working memory instead. One explanation for this difference might have to do with the sophistication of children’s counterfactual reasoning skills at these different ages. Taken together, these results suggest that CR does not develop as a unique capacity, but instead likely relies on EFs that influence different CR skills in distinct ways across development. This represents an initial step in understanding early CR skills, which are promising contributors to emerging scientific literacy. 

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3. Emergence of relational reasoning

   https://doi.org/doi.org/10.1016/j.cobeha.2020.11.012  

   Holyoak, K. J.; Lu, H. (February 2021, Current opinion in behavioral sciences)

   null (Ed.)

   We review recent theoretical and empirical work on the emergence of relational reasoning, drawing connections among the fields of comparative psychology, developmental psychology, cognitive neuroscience, cognitive science, and machine learning. Relational learning appears to involve multiple systems: a suite of Early Systems that are available to human infants and are shared to some extent with nonhuman animals; and a Late System that emerges in humans only, at approximately age three years. The Late System supports reasoning with explicit role-governed relations, and is closely tied to the functions of a frontoparietal network in the human brain. Recent work in cognitive science and machine learning suggests that humans (and perhaps machines) may acquire abstract relations from nonrelational inputs by means of processes that enable re-representation. 

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4. Exploring potential cognitive foundations of scientific literacy inpreschoolers: Causal reasoning and executive function

   https://doi.org/10.1016/j.ecresq.2018.09.007  

   Bauer, Jessie-Raye; Booth, Amy E. (January 2019, Early childhood research quarterly)

   Despite increasing emphasis in the United States on promoting student engagement and achievement inscience, technology, engineering, and mathematics (STEM) fields, the origins of scientific literacy remainpoorly understood. We begin to address this limitation by considering the potential contributions oftwo distinct domain-general skills to early scientific literacy. Given their relevance to making predic-tions and evaluating evidence, we consider the degree to which causal reasoning skills relate to scientificliteracy (as measured by an adaptive standardized test specifically designed for preschoolers). We alsoconsider executive function (EF) as a potentially more fundamental contributor. While previous researchhas demonstrated that EF is predictive of achievement in other core academic domains like reading andmath, its relationship to scientific literacy, particularly in early childhood, has received little attention. Toexamine how causal reasoning and EF together potentially relate to the development of scientific literacyin young children, we recruited 125 3-year-olds to complete three causal reasoning tasks, three EF tasks,and the aforementioned measure of scientific literacy. Results from a series of hierarchical regressionsrevealed that EF, and one measure of causal reasoning (causal inferencing) were related to scientific liter-acy, even after controlling for age, ethnicity, maternal education, and vocabulary knowledge. Moreover,causal inferencing ability was a significant partial mediator between EF and scientific literacy. Althoughadditional research will be required to further specify the nature of these relationships, the current worksuggests that EF has the potential to support scientific literacy, perhaps in part, by scaffolding causalreasoning skills. 

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5. Learning Action-Effect Dynamics for Hypothetical Vision-Language Reasoning Task

   Sampat, Shailaja; Banerjee, Pratyay; Yang, Yezhou; and Baral, Chitta. (December 2022, Findings of EMNLP 2022.)

   Actions’ play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform ‘Reasoning about Actions & Change’ (RAC). This has been an important research direction in Artificial Intelligence (AI) in general, but the study of RAC with visual and linguistic inputs is relatively recent. The CLEVR_HYP is one such testbed for hypothetical vision-language reasoning with actions as the key focus. In this work, we propose a novel learning strategy that can improve reasoning about the effects of actions. We implement an encoder-decoder architecture to learn the representation of actions as vectors. We combine the aforementioned encoder-decoder architecture with existing modality parsers and a scene graph question answering model to evaluate our proposed system on the CLEVR_HYP dataset. We conduct thorough experiments to demonstrate the effectiveness of our proposed approach and discuss its advantages over previous baselines in terms of performance, data efficiency, and generalization capability. 

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